Transfer learning for recommendation. To alleviate the data sparsity and cold-start issues that broadly exist in recommender systems, researchers have explored the idea of knowledge transfer from other domains [1]}, [2]}, [3]}, markets [4]} or platforms [5]} for improving the recommendation at the target domain. Existing approaches mainly rely on shared information between the source and target domains for conducting the transfer, e.g., common users [6]}, [7]}, [8]}, [9]}, items [10]}, [1]} or attributes [12]}. Recently, pre-trained language models [13]}, [14]} (PLM) have shown to be general semantic bridge to connect different tasks or domains [15]}, and several studies propose to encode the associated text of items by PLMs as universal item representations [16]}, [17]}, [18]}. Based on textual (or multimodal [19]}) item representations, one can pre-train recommenders on multiple domains and transfer the fused knowledge to downstream domains without overlapping users or items.
However, these studies ususally enforce a tight binding between the text encodings from PLMs and the final item representations, which might over-emphasize the effect of text features. In contrast, we propose a novel two-step representation scheme based on discrete codes, which is endowed with a more strong representation capacity for enhancing the cross-domain recommendation.
